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Article Name A Computational Framework to Investigate The Effect of Robotic Assistance on Human Muscular Effort
Author Name Emel DEMIRCAN1
Address 1Bahcesehir Üniversitesi Müh. Fak. Mekatronik. Müh. Böl., Istanbul/TÜRKİYE
Abstract Human motor performance is a key area of investigation in both biomechanics and humanoid robotics. In robotics, understanding human neuromuscular control is important to synthesize prosthetic motions and ensure safe human-robot interaction. Building controllable biomechanical models through modeling and algorithmic tools from both robotics and biomechanics increases our scientific understanding of neuro-musculoskeletal mechanics and control. The resulting models can consequently help quantifying the characteristics of a subject’s motion and in designing effective treatments, like motion training. The objective of this paper is to explore how neural control dictates motor performance in humans by developing a computational framework that enables robotics-based control and simulation of the human musculoskeletal system. More specifically: (1) computational models of the human musculoskeletal system for robotics-based control were developed; (2) performance metrics were integrated for motion characterization based on a subject’s physiological constraints; and (3) robust control and simulation algorithms were integrated to synthesize movement using biomechanical models that accurately match experimental data. Motion capture experiments were conducted to tune subject-specific parameters. To investigate the effects of robotic assistance as a means of increasing the efficiency in motor movements an experiment was designed in which subjects will initially performed a basic task and then performed the same task with the assistance of the six degrees-of-freedom JACO robotic arm. Initial results showed that robotic assistance was efficient in decreasing the muscle activation for major arm muscles. Keywords : Muscle activation; Robotics; Musculoskeletal modeling; Dynamic simulation.
Published in Electronic Journal of Machine Technologies
Issue 13
Volume 4
Pages 31-44
Year 2016
Type Paper
Language English
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